plot_results

---

method plot_results(models=None, metric=None, title=None, legend="lower right", figsize=None, filename=None, display=True)[source]

Plot the model results.

If all models applied bootstrap, the plot is a boxplot. If not, the plot is a barplot. Models are ordered based on their score from the top down. The score is either the score_bootstrap or score_test attribute of the model, selected in that order.

Parameters

models: int, str, Model, slice, sequence or None, default=None Models to plot. If None, all models are selected. metric: int, str, sequence or None, default=None Metric to plot (only for multi-metric runs). Other available options are "time_bo", "time_fit", "time_bootstrap" and "time". If str, add + between options to select more than one. If None, the metric used to run the pipeline is selected. title: str, dict or None, default=None Title for the plot. If None, no title is shown. If str, text for the title. If dict, title configuration. legend: str, dict or None, default="lower right" Legend for the plot. See the user guide for an extended description of the choices. If None: No legend is shown. If str: Location where to show the legend. If dict: Legend configuration. figsize: tuple or None, default=None Figure's size in pixels, format as (x, y). If None, it adapts the size to the number of models. filename: str or None, default=None Save the plot using this name. Use "auto" for automatic naming. The type of the file depends on the provided name (.html, .png, .pdf, etc...). If filename has no file type, the plot is saved as html. If None, the plot is not saved. display: bool or None, default=True Whether to render the plot. If None, it returns the figure.

Returns

go.Figure or None Plot object. Only returned if display=None.

See Also

plot_confusion_matrix

Plot a model's confusion matrix.

plot_probabilities

Plot the probability distribution of the target classes.

plot_threshold

Plot metric performances against threshold values.

Example

>>> from atom import ATOMClassifier
>>> from sklearn.datasets import make_classification

>>> X, y = make_classification(n_samples=1000, flip_y=0.2, random_state=1)

>>> atom = ATOMClassifier(X, y, random_state=1)
>>> atom.run(["GNB", "LR", "RF", "LGB"], metric=["f1", "recall"])
>>> atom.plot_results()

>>> atom.run(["GNB", "LR", "RF", "LGB"], metric=["f1", "recall"], n_bootstrap=5)
>>> atom.plot_results()

>>> atom.plot_results(metric="time_fit+time")